Biometric Cyber Competence Assessment via Neural Network
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Solution Overview
Problem
Existing methods for assessing cybersecurity awareness or cyber competence require active user involvement, which can be challenging for individuals with impaired mental states, and do not effectively monitor cyber vulnerability in real-time, especially when connected to public or private networks.
Innovation Solution
A system and method using a neural network model to analyze human biometrics, such as eye movement data, to assess cyber competence without active user participation, employing an adaptive neural network model that correlates eye movement patterns with mental disorders to determine a cyber competence score and control network access accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If active user involvement is required for cybersecurity assessment, then assessment accuracy can be improved, but usability deteriorates for individuals with impaired mental states
Solution Approach 1:
The patent replaces active user participation (mechanical interaction) with passive biometric monitoring (optical/electrical sensing). Eye tracking devices and other biometric sensors automatically capture physiological data without requiring user action, thereby maintaining assessment accuracy while eliminating the operational burden on users with impaired mental states.
Solution Approach 2:
The system enables self-assessment by having users passively wear biometric monitoring devices during normal activities. The system automatically collects, analyzes, and interprets biometric data to generate cybersecurity risk assessments without requiring users to actively engage in evaluation tasks or self-report their state.
2Measurement precision
If dedicated evaluation tests are administered, then cyber vulnerability can be assessed, but continuous monitoring capability is lost
Solution Approach 1:
The patent implements continuous biometric monitoring that operates throughout the user's daily activities rather than during discrete evaluation sessions. The eye tracking and biometric sensors continuously capture data, enabling the system to assess cyber vulnerability at any moment and track changes over time, thereby providing both precision and continuous coverage.
3Productivity
If traditional cybersecurity assessment methods are used, then active user engagement is ensured, but real-time monitoring of cyber vulnerability is not achieved
Solution Approach 1:
The system implements real-time feedback loops where biometric data is continuously collected, analyzed, and used to update cybersecurity risk assessments dynamically. The system can immediately detect changes in the user's mental state and adjust security measures accordingly, ensuring both efficiency and real-time reliability in vulnerability detection.
Data Source
AI summary
The present invention discloses a system and method for assessing the cyber competence of a user by analyzing human biometrics using a neural network model. According to an embodiment, the system collects human biometrics data, including eye movement data from biometric sensing device of a user, analyses the human biometrics data using an adaptive neural network model that performs rational inference to learn the correlation between the human biometric and mental disorder and provide an assessment of a cyber competence for the user based on identified mental disorder. The system may identify definitive cyber risk and assign a cyber competence score for the user using a machine learning model or using a mental disorder-cyber risk correlation table. The system may take preventive actions to prevent cyber-attacks based on the cyber competence score.